Madhura Datta

Bio: Madhura Datta is an academic researcher from University of Calcutta. The author has contributed to research in topics: Spoofing attack & Face (geometry). The author has an hindex of 3, co-authored 7 publications receiving 14 citations.

Class Specific Threshold Selection in Face Space Using Set Estimation Technique for RGB Color Components

Abstract: In conventional face recognition techniques, any query image is always classified to one of the face classes irrespective of whether the query image is a face or not. Most of the recognition algorithms are dissimilarity based, and one needs to put a proper threshold on the dissimilarity value for the sake of classification. In this paper, we have introduced a novel thresholding technique for classification, where the query image is not necessarily classified to one of the classes in the training set. The theoretical formulation of the thresholding technique and its utility are demonstrated on color face and non face datasets with RGB color components as features in the subspace. The proposed threshold selection is based on statistical method of set estimation and is guided by minimal spanning tree. Experiment shows that the proposed class specific threshold based technique performs better than the non threshold based systems using subspace algorithms.

Colour band fusion and region enhancement of spectral image using multivariate histogram

Abstract: Multi-spectral satellite remote sensing imagery have several applications including detection of objects or distinguishing land surface areas based on amount of greenery or water etc. The enhanceme...

Fusion of face and iris features for multimodal biometrics

Abstract: The recognition accuracy of a single biometric authentication system is often much reduced due to the environment, user mode and physiological defects. In this paper, we combine face and iris features for developing a multimode biometric approach, which is able to diminish the drawback of single biometric approach as well as to improve the performance of authentication system. We combine a face database ORL and iris database CASIA to construct a multimodal biometric experimental database with which we validate the proposed approach and evaluate the multimodal biometrics performance. The experimental results reveal the multimodal biometrics verification is much more reliable and precise than single biometric approach.

DIMMA: A Design and Implementation Methodology for Metaheuristic Algorithms-A Perspective from Software Development

Abstract: Metaheuristic algorithms will gain more and more popularity in the future as optimization problems are increasing in size and complexity. In order to record experiences and allow project to be replicated, a standard process as a methodology for designing and implementing metaheuristic algorithms is necessary. To the best of the authors' knowledge, no methodology has been proposed in literature for this purpose. This paper presents a Design and Implementation Methodology for Metaheuristic Algorithms, named DIMMA. The proposed methodology consists of three main phases and each phase has several steps in which activities that must be carried out are clearly defined in this paper. In addition, design and implementation of tabu search metaheuristic for travelling salesman problem is done as a case study to illustrate applicability of DIMMA.

Non-visual programming, perceptual culture and mulsemedia: case studies of five blind computer programmers

Abstract: This chapter describes an investigation into the premise that blind programmers and web-developers can create modern Graphical User Interfaces (GUI) through perceptions of MulSeMedia, and whether perceptual culture has a role in this understanding. Its purpose it to: 1) investigate whether the understanding of computer interfaces is related to perceptual culture as well as perceptual ability; 2) investigate whether it is possible for a person who has never seen to understand visual concepts in informational technology through non-visual senses and memories; and 3) provoke questions as to the nature of computer interfaces, and whether they can ever be regarded as MulSeMedia style interfaces. Beyond this, it proposes to: 1) inform accessible MulSeMedia interface design; and 2) investigate the boundaries of accessing computer interfaces through non-visual perceptions and memories. In order to address these aims and objectives, this chapter discusses the following two research questions:1) Is the perceptual culture of a blind person as important as physical level of blindness in being able to understand, work with, learn how to use or create and program Graphical User Inerfaces (GUIs)?2) Can a cultural model of understanding blindness in part explain the difficulties in adapting Windows MulSeMedia applications for blind people? The study found that programmers who had been introduced to, and educated using a range of visual, audio and /or tactile devices, whether early or late blind, could adapt to produce code with GUIs, but programmers who were educated using only tactile and audio devices preferred to shun visual references in their work.

Petri Nets and Discrete Events Systems

Abstract: This paper provides an overview over the relationship between Petri Nets and Discrete Event Systems as they have been proved as key factors in the cognitive processes of perception and memorization. In this sense, different aspects of encoding Petri Nets as Discrete Dynamical Systems that try to advance not only in the problem of reachability but also in the one of describing the periodicity of markings and their similarity, are revised. It is also provided a metric for the case of Non-bounded Petri Nets.

Natural image understanding using algorithm selection and high-level feedback

Abstract: Natural Image processing and understanding encompasses hundreds or even thousands of different algorithms. Each algorithm has a certain peak performance for a particular set of input features and configurations of the objects/regions of the input image (environment). To obtain the best possible result of processing, we propose an algorithm selection approach that permits to always use the most appropriate algorithm for the given input image. This is obtained by at first selecting an algorithm based on low level features such as color intensity, histograms, spectral coefficients. The resulting high level image description is then analyzed for logical inconsistencies (contradictions) that are then used to refine the selection of the processing elements. The feedback created from the contradiction information is executed by a Bayesian Network that integrates both the features and a higher level information selection processes. The selection stops when the high level inconsistencies are all resolved or no more different algorithms can be selected.